Staff Software Engineer AI Reliability Engineering

About Anthropic

Anthropics mission is to create reliable interpretable and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers engineers policy experts and business leaders working together to build beneficial AI systems.

About the role

Anthropic is seeking talented and experienced Reliability Engineers including Software Engineers and Systems Engineers with experience and interest in reliability to join our team. We will be defining and achieving reliability metrics for all of Anthropics internal and external products and services. While significantly improving reliability for Anthropics services we plan to use the developing capabilities of modern AI models to reengineer the way we work. This team will be a critical part of Anthropics mission to bring the capabilities of groundbreaking AI technologies to benefit humanity in a safe and reliable way.

Responsibilities:

• Develop appropriate Service Level Objectives for large language model serving and training systems balancing availability/latency with development velocity.
• Design and implement monitoring systems including availability latency and other salient metrics.
• Assist in the design and implementation of highavailability language model serving infrastructure capable of handling the needs of millions of external customers and hightraffic internal workloads.
• Develop and manage automated failover and recovery systems for model serving deployments across multiple regions and cloud providers.
• Lead incident response for critical AI services ensuring rapid recovery and systematic improvements from each incident
• Build and maintain cost optimization systems for largescale AI infrastructure focusing on accelerator (GPU/TPU/Trainium) utilization and efficiency

You may be a good fit if you:

• Have extensive experience with distributed systems observability and monitoring at scale
• Understand the unique challenges of operating AI infrastructure including model serving batch inference and training pipelines
• Have proven experience implementing and maintaining SLO/SLA frameworks for businesscritical services
• Are comfortable working with both traditional metrics (latency availability) and AIspecific metrics (model performance training convergence)
• Have experience with chaos engineering and systematic resilience testing
• Can effectively bridge the gap between ML engineers and infrastructure teams
• Have excellent communication skills.

Strong candidates may also:

• Have experience operating largescale model training infrastructure or serving infrastructure >1000 GPUs)
• Have experience with one or more ML hardware accelerators (GPUs TPUs Trainium e.g.
• Understand MLspecific networking optimizations like RDMA and InfiniBand.
• Have expertise in AIspecific observability tools and frameworks
• Understand ML model deployment strategies and their reliability implications
• Have contributed to opensource infrastructure or ML tooling

Deadline to apply:None. Applications will be reviewed on a rolling basis.

Logistics

Education requirements: We require at least a Bachelors degree in a related field or equivalent experience.

Locationbased hybrid policy: Currently we expect all staff to be in one of our offices at least 25 of the time. However some roles may require more time in our offices.

Visa sponsorship:We do sponsor visas! However we arent able to successfully sponsor visas for every role and every candidate. But if we make you an offer we will make every reasonable effort to get you a visa and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy so we urge you not to exclude yourself prematurely and to submit an application if youre interested in this work. We think AI systems like the ones were building have enormous social and ethical implications. We think this makes representation even more important and we strive to include a range of diverse perspectives on our team.

How were different

We believe that the highestimpact AI research will be big science. At Anthropic we work as a single cohesive team on just a few largescale research efforts. And we value impact advancing our longterm goals of steerable trustworthy AI rather than work on smaller and more specific puzzles. We view AI research as an empirical science which has as much in common with physics and biology as with traditional efforts in computer science. Were an extremely collaborative group and we host frequent research discussions to ensure that we are pursuing the highestimpact work at any given time. As such we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic including: GPT3 CircuitBased Interpretability Multimodal Neurons Scaling Laws AI & Compute Concrete Problems in AI Safety and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits optional equity donation matching generous vacation and parental leave flexible working hours and a lovely office space in which to collaborate with colleagues.